Peptide Microarray Analysis ofIn Silico-Predicted Epitopes for Serological Diagnosis of Toxoplasma gondii Infection in Humans

Abstract

Toxoplasma gondii infections occur worldwide in humans and animals. In immunocompromised or prenatally infected humans, T. gondii can cause severe clinical symptoms. The identification of specific epitopes on T. gondii antigens is essential for the improvement and standardization of the serological diagnosis of toxoplasmosis. We selected 20 peptides mimicking linear epitopes on GRA1, GRA2, GRA4, and MIC3 antigenic T. gondii proteins in silico using the software ABCpred. A further 18 peptides representing previously published epitopes derived from GRA1, SAG1, NTPase1, and NTPase2 antigens were added to the panel. A peptide microarray assay was established to prove the diagnostic performance of the selected peptides with human serum samples. Seropositive human serum samples (n = 184) were collected from patients presenting with acute toxoplasmosis (n = 21), latent T. gondii infection (n = 53), and inactive ocular toxoplasmosis (n = 10) and from seropositive forest workers (n = 100). To adjust the cutoff values for each peptide, sera from seronegative forest workers (n = 75) and patients (n = 65) were used. Univariate logistic regression suggested the significant diagnostic potential of eight novel and two previously published peptides. A test based on these peptides had an overall diagnostic sensitivity of 69% (100% in ocular toxoplasmosis patients, 86% in acutely infected patients, 81% in latently infected patients, and 57% in seropositive forest workers). The analysis of seronegative sera performed with these peptides revealed a diagnostic specificity of 84%. The results of our study suggest that the use of a bioinformatic approach for epitope prediction in combination with peptide microarray testing is a powerful method for the selection of T. gondii epitopes as candidate antigens for serological diagnosis.